In radiation therapy planning, creating a patient specific treatment plan can be a time consuming and tedious task. Many of the steps are redundant and vary little from patient to patient or plan to plan. Many of these steps can be automated using macro languages or scripts, but certain aspects are difficult without tools for writing logical expressions, loops, and other common programming functionality.
In the past decade, technological advancements have provided a big leap in the field of intensity modulated radiation therapy (IMRT), intensity modulated proton therapy (IMPT) and the like, to improve dose delivery. One area that is difficult to automate in current treatment planning is intensity-modulated radiation therapy (IMRT) or volumetric-modulated arc therapy (VMAT) optimization. Recently the research interest has shifted towards methods of automating various tasks involved in plan generation, starting from beam placement to dose optimization, to assist and reduce the workload burden on the clinical user. Optimization is an iterative process where a user attempts to specify planning goals in the form of dose or biological objectives to create an ideal dose to target structures, typically a uniform high dose, and minimize the dose to critical structures.
Plan evaluation is classified into three phases: 1. Physical evaluation, 2. Technical evaluation and 3. Clinical evaluation. The physical and technical aspects of a plan are generally examined by a technician after the completion of the plan. The clinical aspects of a plan are investigated by a radiation oncologist. Currently an IMRT plan is evaluated based on five categories that cover the physical, technical and clinical aspects of a plan: 1. Geometric analysis, 2. Dose distribution analysis, 3. Dose Volume Histogram (DVH) analysis, 4. Parametric analysis and 5. Deliverability analysis.
The geometric analysis is performed to evaluate the optimality of beams placement. Beam placement is a very important step. The quality of optimization is influenced by the number of beams and their angles. Rules have been formulated for optimal beam placement in IMRT in view of increasing the optimality and deliverability of an IMRT plan.
The dose distribution analysis qualitatively verifies the optimality of dose distribution in axial, coronal and saggital planes. This analysis can be further split up into 2D analysis and 3D analysis. 2D dose distribution analysis implies the evaluation of dose distribution slice-by-slice. This type of analysis is used to evaluate the conformality of the prescribed dose with respect to the target volume in each slice. This type of analysis can also reveal the distribution of cold or hot spots in and around the target volume. Cold or hot spots are areas within the target and organs at risk that receive less or greater than the intended dose of radiation. The 3D distribution analysis is useful in determining how conformal a dose distribution is to the overall target volume with respect to a set of beam orientations.
Dose Volume Histograms (DVH) are a powerful tool for evaluating the optimality of a plan. A DVH represents a 3-dimensional dose distribution in a graphical 2-dimensional format. A DVH for target volume graphically represents the quality of the dose distribution in terms of coverage, conformity and homogeneity. The DVH curves for Organs-at-risk (OARs) represent the efficiency at which the OARs are spared in terms of mean and maximum dose.
The parametric analysis is performed to quantitatively verify the optimality of dose. The parameters used in this analysis are: (a) minimum, mean and maximum dose for target volume and OARs and (b) coverage, conformity and homogeneity indices for target volume. Apart from physical metrics for plan evaluation, a plurality of biological metrics are used in plan evaluation. These biological metrics include Equivalent Uniform Dose (EUD), Tumor Control Probability (TCP) and Normal Tissue Complication Probability (NTCP) and the like.
Deliverability analysis is performed in order to evaluate how robust the plan is in terms of dose delivery. This analysis involves the verification of parameters such as number of segments, minimum or average monitor units (MU) per segment, Minimum Segment Area (MSA), total delivery time and the like. MU is a measure of machine output of a linear accelerator in radiation therapy. The deliverability analysis reveals whether or not a plan is realistically deliverable.
Various stages of plan generation have been automated with different techniques. These techniques reduce the burden on the clinical user, i.e. a radiation technician, by automating the plan generation process, such as dose objective manipulation and IMRT/VMAT optimization. Given the complexity involved with radiation therapy treatment plan generation, it is imperative that the user wants a certain amount of manual control and review but at the same time it stops these techniques from being fully automatic. A current auto-planning solution offers one time configuration of user defined template which can be later applied to a new patient for automatically generating a treatment plan.
Specifically, it is difficult to determine the best plan to meet the goals since the definition of best is subjective and variable for the same user from patient to patient. After plan generation, the user weighs various tradeoffs between target goals and organs at risk goals and decides what is acceptable for each patient. Understanding the tradeoffs has been the focus of several technologies. However, one issue with the approaches is that the user has too much more flexibility than needed which makes the workflow too general and less focused for physicians. The present application relates generally to medical imaging. It finds particular application in calibration of a positron emission tomagraphy (PET) detectors, and will be described with particular reference thereto. However, it is to be understood that it also finds application in other usage scenarios and is not necessarily limited to the aforementioned application. The present application combines the shape based method of initializing radiation treatment planning optimization parameters with the progressive tuning of the optimization parameters. It also uses shape based DVH predictions to identify potential trade-offs and provide plan quality QA.
Using shape based methods to determine optimization parameters is limited such that the parameters retrieved are only those used in prior plans (or models) and requires specific knowledge available for the type of treatment desired. The source of the knowledge is not necessarily optimal or driven to the limit. However the shape based method captures trade-offs made in the approved plans and is relatively fast.
Progressive tuning of optimization parameters has the limitation that the initial conditions are the same for each patient and can take significant time to reach the final solution. However, the algorithm strives to drive optimization parameters to the limit.